We argue that robustness of explanations---ie, that similar inputs should give rise to similar
explanations---is a key desideratum for interpretability. We introduce metrics to quantify …

At the turn of the 21st century, Susan Leigh Anderson and Michael Anderson conceived and
introduced the Machine Ethics research program, that aimed to highlight the requirements …

The most successful Machine Learning (ML) systems remain complex black boxes to end-
users, and even experts are often unable to understand the rationale behind their decisions …

In the near future, the capabilities of commonly used artificial systems will reach a level
where we will be able to permit them to make moral decisions autonomously as part of their …

The evaluation of explanation methods has become a significant issue in explainable
artificial intelligence (XAI) due to the recent surge of opaque AI models in decision support …

This paper examines the metaethical dimensions of the computing community's efforts to
program ethical decision-making abilities into robots. Arguments for and against that …

This paper focuses on the research field of machine ethics and how it relates to a
technological singularity—a hypothesized, futuristic event where artificial machines will have …

Abstract Using Asimov's “Bicentennial Man” as a springboard, a number of metaethical
issues concerning the emerging field of machine ethics are discussed. Although the ultimate …

As artificial intelligence and machine learning algorithms become increasingly prevalent in
society, multiple stakeholders are calling for these algorithms to provide explanations. At the …

This paper surveys the state-of-the-art in machine ethics, that is, considerations of how to
implement ethical behaviour in robots, unmanned autonomous vehicles, or software …